1. Field of the Invention
The present invention relates generally to systems and methods for managing data networks, and more particularly, to methods and systems for managing network data traffic.
2. Description of the Related Art
Computer networks are very common. Computer networks can be small local area networks (LAN) connecting only a few computers or can be wide area networks (WAN) connecting an entire enterprise of multiple LANs. The Internet can also be considered as a very large WAN. As the number of computers and users on the network increases then the network data traffic will also increase. As the network data traffic increases then “choke” points can develop at a network node where data flow is restricted due to some shortfall in that node. The choke point slows down the data traffic. For example, a server is used to serve data to requesting clients via the computer network that interconnects the server and the clients. As the number of clients increases, the volume of data being served by the server also increases. Eventually, the volume of data being requested by the clients becomes greater than the volume of data the server can serve in a timely manner. As a result, the server delays sending the requested data and the data throughput of the network is choked by the limited data throughput capability of the server. Similarly, any other node can become a choke point when the output demands on the node become greater than the output capabilities of the node. A properly designed network minimizes choke points so as to maximize data flow. In addition an accurate understanding of network data traffic allows the network data traffic to be distributed more evenly across servers, routers and other network nodes.
Traditionally, network traffic predictions have been based on a Poisson distribution pattern. A Poisson distribution is a probability density function that is often used as a mathematical model of the number of outcomes obtained in a suitable interval of time and space. A poison distribution has its mean equal to its variance, that is used as an approximation to the binomial distribution, and that has the form of:f(x)=e−μμx/x!  Formula 1
where μ is the mean and x takes on nonnegative integral values.
FIGS. 1A, 1B and 1C show a typical Poisson distribution pattern of data traffic 100 in a network at three different time bases. FIG. 1A shows the data traffic 100 at a one hundred second time base. The data shown appears to be approximately uniform in density (measured in packets/unit time on the Y-axis) and frequency (on the X-axis) therefore resulting in a generally uniform appearing graph with no significant peaks or valleys.
FIG. 1B shows the same data traffic 100 with a time base of one second having approximately the same pattern. Again the data traffic shown in FIG. 1B appears to be approximately uniform in density and frequency therefore resulting in a uniform appearing graph but with what appears to be very minor peaks and valleys that are very closely spaced.
FIG. 1C shows the same data traffic 100 with a time base of 0.01 second that shows some periodic variations in the data distribution such as periodic peaks 110A, 110B and periodic valleys 112A, 112B.
In sum, a Poisson distribution appears approximately uniform at a large time base (e.g., one hundred seconds) with some periodic peaks and valleys at a relatively small time base (e.g., 0.01 seconds).
When a network is being designed, a Poisson pattern is traditionally used to model the predicted data traffic in the network. The Poisson model has also been used when managing and operating networks such as to determine optimum times for back-up and network interruption for repairs or identify network nodes needing improvement so as to avoid a choke point developing. An accurate data traffic pattern projection can also provide insight into other aspects of the network operations such as load balancing and other operations.
However, actual studies of actual network data traffic show the data traffic actually follows a pattern that has peaks and valleys at any time base rather than a Poisson pattern as shown in FIGS. 1A–C above. For example, one study by Will E. Leland, Murad S. Taqqu, Walter Willinger, and Daniel V. Wilson, and entitled “On the Self-Similar Nature of Ethernet Traffic (Extended Version)”, IEEE/ACM Trans. Networking, Vol. 2., pp. 1–15, January 1994 (hereafter referred to as Leland) is incorporated by reference herein in its entirety for all purposes. Leland examined data packet traffic flow in an Ethernet LAN.
FIGS. 2A and 2B show a graph 200 of the data packet traffic flow that Leland actually measured at different time bases. FIG. 2A shows the graph 200 with a time base of one hundred seconds. Even at a one hundred second time base, significant peaks 210A, 210B, 210C, 210D and valleys 212A, 212B, 212C, 212D are evident. As the time base is decreased to 0.01 seconds, in FIG. 2B, significant peaks 210E, 210F, 210G, 210H and valleys 212E, 212F, 212G, 212H are also shown. Because the presence of peaks 210A–H and valleys 212A–H are constant, regardless of the time base, the patterns can also be said to be self-similar in that they have approximately the same form regardless of time base.
Another study of interest is by Vern Paxson and Sally Floyd, and entitled “Wide Area Traffic: The Failure of Poisson Modeling”, IEEE/ACM Trans. Networking, Vol. 3, pp. 226–244, June 1995 (hereafter referred to as Paxson) which is incorporated by reference herein in its entirety for all purposes. Paxson examined WAN network traffic. Paxson also found that Poisson was not sufficiently accurate model of packet data transfer, which makes up the bulk of WAN data traffic. Paxson also identified a bursty (i.e., having peaks and valleys), self-similar pattern to the packet data transfer through the WAN which is similar to the data traffic flow wave forms found by Leland above.
A study of Internet packet data traffic by Mark E. Crovella and Azer Bestavros, and entitled “Self-Similarity in WWW traffic: Evidence and Possible Causes,” IEEE/ACM Trans. Networking, Vol. 5, pp 835–846, December 1997, (Crovella), is incorporated by reference herein in its entirety for all purposes. Crovella found that packet data traffic on the world wide web also followed a bursty, self-similar pattern and not a Poisson pattern.
Each of the studies (Leland, Paxson and Crovella) showed that Poisson models do not accurately represent or predict actual packet data flow patterns. In view of the foregoing, there is a need for a system and method of more accurately predicting network data traffic.